{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入相关包\n",
    "\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 21)"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读入数据\n",
    "data = pd.read_csv('/Users/fq/Desktop/WA_Fn-UseC_-Telco-Customer-Churn.csv')\n",
    "# 查看数据集大小\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
       "3  7795-CFOCW    Male              0      No         No      45           No   \n",
       "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
       "0  No phone service             DSL             No  ...               No   \n",
       "1                No             DSL            Yes  ...              Yes   \n",
       "2                No             DSL            Yes  ...               No   \n",
       "3  No phone service             DSL            Yes  ...              Yes   \n",
       "4                No     Fiber optic             No  ...               No   \n",
       "\n",
       "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0          No          No              No  Month-to-month              Yes   \n",
       "1          No          No              No        One year               No   \n",
       "2          No          No              No  Month-to-month              Yes   \n",
       "3         Yes          No              No        One year               No   \n",
       "4          No          No              No  Month-to-month              Yes   \n",
       "\n",
       "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
       "0           Electronic check          29.85         29.85    No  \n",
       "1               Mailed check          56.95        1889.5    No  \n",
       "2               Mailed check          53.85        108.15   Yes  \n",
       "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
       "4           Electronic check          70.70        151.65   Yes  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数据前五行\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        7043 non-null   object \n",
      " 1   gender            7043 non-null   object \n",
      " 2   SeniorCitizen     7043 non-null   int64  \n",
      " 3   Partner           7043 non-null   object \n",
      " 4   Dependents        7043 non-null   object \n",
      " 5   tenure            7043 non-null   int64  \n",
      " 6   PhoneService      7043 non-null   object \n",
      " 7   MultipleLines     7043 non-null   object \n",
      " 8   InternetService   7043 non-null   object \n",
      " 9   OnlineSecurity    7043 non-null   object \n",
      " 10  OnlineBackup      7043 non-null   object \n",
      " 11  DeviceProtection  7043 non-null   object \n",
      " 12  TechSupport       7043 non-null   object \n",
      " 13  StreamingTV       7043 non-null   object \n",
      " 14  StreamingMovies   7043 non-null   object \n",
      " 15  Contract          7043 non-null   object \n",
      " 16  PaperlessBilling  7043 non-null   object \n",
      " 17  PaymentMethod     7043 non-null   object \n",
      " 18  MonthlyCharges    7043 non-null   float64\n",
      " 19  TotalCharges      7043 non-null   object \n",
      " 20  Churn             7043 non-null   object \n",
      "dtypes: float64(1), int64(2), object(18)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# 查看数据信息\n",
    "data.info()\n",
    "# 数据共21个特征，7043行，占用内存为1.1M\n",
    "# 观察数据发现，TotalCharges’总消费额的数据类型为字符串，应该转换为浮点型数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data[['TotalCharges']].astype(float)\n",
    "# ValueError: could not convert string to float:无法转换成浮点型数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "customerID 的行数是：7043\n",
      "customerID 的数据类型是：object\n",
      "customerID 的内容是：\n",
      "8294-UIMBA    1\n",
      "3177-LASXD    1\n",
      "9681-KYGYB    1\n",
      "1591-MQJTP    1\n",
      "2929-ERCFZ    1\n",
      "             ..\n",
      "5351-QESIO    1\n",
      "8541-QVFKM    1\n",
      "9103-CXVOK    1\n",
      "8165-CBKXO    1\n",
      "4134-BSXLX    1\n",
      "Name: customerID, Length: 7043, dtype: int64\n",
      "\n",
      "gender 的行数是：7043\n",
      "gender 的数据类型是：object\n",
      "gender 的内容是：\n",
      "Male      3555\n",
      "Female    3488\n",
      "Name: gender, dtype: int64\n",
      "\n",
      "SeniorCitizen 的行数是：7043\n",
      "SeniorCitizen 的数据类型是：int64\n",
      "SeniorCitizen 的内容是：\n",
      "0    5901\n",
      "1    1142\n",
      "Name: SeniorCitizen, dtype: int64\n",
      "\n",
      "Partner 的行数是：7043\n",
      "Partner 的数据类型是：object\n",
      "Partner 的内容是：\n",
      "No     3641\n",
      "Yes    3402\n",
      "Name: Partner, dtype: int64\n",
      "\n",
      "Dependents 的行数是：7043\n",
      "Dependents 的数据类型是：object\n",
      "Dependents 的内容是：\n",
      "No     4933\n",
      "Yes    2110\n",
      "Name: Dependents, dtype: int64\n",
      "\n",
      "tenure 的行数是：7043\n",
      "tenure 的数据类型是：int64\n",
      "tenure 的内容是：\n",
      "1     613\n",
      "72    362\n",
      "2     238\n",
      "3     200\n",
      "4     176\n",
      "     ... \n",
      "28     57\n",
      "39     56\n",
      "44     51\n",
      "36     50\n",
      "0      11\n",
      "Name: tenure, Length: 73, dtype: int64\n",
      "\n",
      "PhoneService 的行数是：7043\n",
      "PhoneService 的数据类型是：object\n",
      "PhoneService 的内容是：\n",
      "Yes    6361\n",
      "No      682\n",
      "Name: PhoneService, dtype: int64\n",
      "\n",
      "MultipleLines 的行数是：7043\n",
      "MultipleLines 的数据类型是：object\n",
      "MultipleLines 的内容是：\n",
      "No                  3390\n",
      "Yes                 2971\n",
      "No phone service     682\n",
      "Name: MultipleLines, dtype: int64\n",
      "\n",
      "InternetService 的行数是：7043\n",
      "InternetService 的数据类型是：object\n",
      "InternetService 的内容是：\n",
      "Fiber optic    3096\n",
      "DSL            2421\n",
      "No             1526\n",
      "Name: InternetService, dtype: int64\n",
      "\n",
      "OnlineSecurity 的行数是：7043\n",
      "OnlineSecurity 的数据类型是：object\n",
      "OnlineSecurity 的内容是：\n",
      "No                     3498\n",
      "Yes                    2019\n",
      "No internet service    1526\n",
      "Name: OnlineSecurity, dtype: int64\n",
      "\n",
      "OnlineBackup 的行数是：7043\n",
      "OnlineBackup 的数据类型是：object\n",
      "OnlineBackup 的内容是：\n",
      "No                     3088\n",
      "Yes                    2429\n",
      "No internet service    1526\n",
      "Name: OnlineBackup, dtype: int64\n",
      "\n",
      "DeviceProtection 的行数是：7043\n",
      "DeviceProtection 的数据类型是：object\n",
      "DeviceProtection 的内容是：\n",
      "No                     3095\n",
      "Yes                    2422\n",
      "No internet service    1526\n",
      "Name: DeviceProtection, dtype: int64\n",
      "\n",
      "TechSupport 的行数是：7043\n",
      "TechSupport 的数据类型是：object\n",
      "TechSupport 的内容是：\n",
      "No                     3473\n",
      "Yes                    2044\n",
      "No internet service    1526\n",
      "Name: TechSupport, dtype: int64\n",
      "\n",
      "StreamingTV 的行数是：7043\n",
      "StreamingTV 的数据类型是：object\n",
      "StreamingTV 的内容是：\n",
      "No                     2810\n",
      "Yes                    2707\n",
      "No internet service    1526\n",
      "Name: StreamingTV, dtype: int64\n",
      "\n",
      "StreamingMovies 的行数是：7043\n",
      "StreamingMovies 的数据类型是：object\n",
      "StreamingMovies 的内容是：\n",
      "No                     2785\n",
      "Yes                    2732\n",
      "No internet service    1526\n",
      "Name: StreamingMovies, dtype: int64\n",
      "\n",
      "Contract 的行数是：7043\n",
      "Contract 的数据类型是：object\n",
      "Contract 的内容是：\n",
      "Month-to-month    3875\n",
      "Two year          1695\n",
      "One year          1473\n",
      "Name: Contract, dtype: int64\n",
      "\n",
      "PaperlessBilling 的行数是：7043\n",
      "PaperlessBilling 的数据类型是：object\n",
      "PaperlessBilling 的内容是：\n",
      "Yes    4171\n",
      "No     2872\n",
      "Name: PaperlessBilling, dtype: int64\n",
      "\n",
      "PaymentMethod 的行数是：7043\n",
      "PaymentMethod 的数据类型是：object\n",
      "PaymentMethod 的内容是：\n",
      "Electronic check             2365\n",
      "Mailed check                 1612\n",
      "Bank transfer (automatic)    1544\n",
      "Credit card (automatic)      1522\n",
      "Name: PaymentMethod, dtype: int64\n",
      "\n",
      "MonthlyCharges 的行数是：7043\n",
      "MonthlyCharges 的数据类型是：float64\n",
      "MonthlyCharges 的内容是：\n",
      "20.05     61\n",
      "19.85     45\n",
      "19.95     44\n",
      "19.90     44\n",
      "20.00     43\n",
      "          ..\n",
      "114.75     1\n",
      "103.60     1\n",
      "113.40     1\n",
      "57.65      1\n",
      "113.30     1\n",
      "Name: MonthlyCharges, Length: 1585, dtype: int64\n",
      "\n",
      "TotalCharges 的行数是：7043\n",
      "TotalCharges 的数据类型是：object\n",
      "TotalCharges 的内容是：\n",
      "           11\n",
      "20.2       11\n",
      "19.75       9\n",
      "19.9        8\n",
      "20.05       8\n",
      "           ..\n",
      "1683.7      1\n",
      "3899.05     1\n",
      "2171.15     1\n",
      "2874.15     1\n",
      "1160.45     1\n",
      "Name: TotalCharges, Length: 6531, dtype: int64\n",
      "\n",
      "Churn 的行数是：7043\n",
      "Churn 的数据类型是：object\n",
      "Churn 的内容是：\n",
      "No     5174\n",
      "Yes    1869\n",
      "Name: Churn, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 依次检查各个字段的数据类型、字段内容和数量。最后发现“TotalCharges”（总消费额）列有11个用户数据缺失。\n",
    "# format函数为字符串的格式化函数\n",
    "for x in data.columns:\n",
    "    test = data.loc[:,x].value_counts()\n",
    "    print('{0} 的行数是：{1}'.format(x,test.sum()))\n",
    "    print('{0} 的数据类型是：{1}'.format(x,data[x].dtypes))\n",
    "    print('{0} 的内容是：\\n{1}\\n'.format(x,test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 采用强制转换，将“TotalCharges”（总消费额）转换为浮点型数据。\n",
    "# 注意在pandas中的数据类型强制转换问题，三种方法：astype（适用于无数据缺失的数据），自定义函数，pandas的函数（注意pandas的版本问题）\n",
    "data[['TotalCharges']]=pd.to_numeric(data['TotalCharges'], errors='coerce').fillna(0)\n",
    "data['TotalCharges'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.162147</td>\n",
       "      <td>32.371149</td>\n",
       "      <td>64.761692</td>\n",
       "      <td>2279.734304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.368612</td>\n",
       "      <td>24.559481</td>\n",
       "      <td>30.090047</td>\n",
       "      <td>2266.794470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.250000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>35.500000</td>\n",
       "      <td>398.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>70.350000</td>\n",
       "      <td>1394.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>89.850000</td>\n",
       "      <td>3786.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>118.750000</td>\n",
       "      <td>8684.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SeniorCitizen       tenure  MonthlyCharges  TotalCharges\n",
       "count    7043.000000  7043.000000     7043.000000   7043.000000\n",
       "mean        0.162147    32.371149       64.761692   2279.734304\n",
       "std         0.368612    24.559481       30.090047   2266.794470\n",
       "min         0.000000     0.000000       18.250000      0.000000\n",
       "25%         0.000000     9.000000       35.500000    398.550000\n",
       "50%         0.000000    29.000000       70.350000   1394.550000\n",
       "75%         0.000000    55.000000       89.850000   3786.600000\n",
       "max         1.000000    72.000000      118.750000   8684.800000"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 数据可视化\n",
    "# 查看流失用户数量和占比\n",
    "plt.rcParams['figure.figsize']=6,6\n",
    "plt.pie(data['Churn'].value_counts(),labels=data['Churn'].value_counts().index,autopct='%1.2f%%',explode=(0.1,0))\n",
    "plt.title('Churn(Yes/No) Ratio')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 用户流失柱状图：\n",
    "churnDf=data['Churn'].value_counts().to_frame()\n",
    "x=churnDf.index\n",
    "y=churnDf['Churn']\n",
    "plt.bar(x,y,width = 0.5)\n",
    "plt.title('Churn(Yes/No) Num')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   SeniorCitizen Churn  percentage of customers\n",
      "0              0    No                 0.640068\n",
      "1              0   Yes                 0.197785\n",
      "2              1    No                 0.094562\n",
      "3              1   Yes                 0.067585\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   gender Churn  percentage of customers\n",
      "0  Female    No                 0.361920\n",
      "1  Female   Yes                 0.133324\n",
      "2    Male    No                 0.372710\n",
      "3    Male   Yes                 0.132046\n"
     ]
    },
    {
     "data": {
      "image/png": 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Cfvvtx4MPPgjAAQccwJgxY1huueU455xzuPjii+nSpQvrr78+Z511Fg8++CBTp05l0KBBC7VvJw4zsw5q5MiRzJw5k+WXX57JkyczceJEdtttNyCNjb755ptzwgkncM4557DNNtvQs2dPTjnllIXerxOHmdkioF+/fgwaNIhbb72Vvn37cscdd9CtWzduvfVW1l57bY4++miuu+46zj77bC699NKF2pcTh5nZIqBLly6cd955bLvttsydO5eePXty1VVXMXPmTPbZZx/OOussZs2axbnnnrvQ+2qTxCHp28CJwPLA88BPIuIxSdsDtwAfFhY/ICKulbQC8AdgNeA2YO+ImCOpN/AA8NWImNYW8ZuZtaTM5bP1UttL7ogRIxgxYsQ8y913332tut+6X1UlaTXgSmC/iOgLHAfcLmlxYF3gdxHRq/C4Nq/6Y2A00B8YDHwjlx8FXOikYWbWPup+xhERL0taPiLeldQVGAD0A5YD1gPuamHV2UDvHGNXYK6kwcBWwMb1jtvMzJrXJvdx5KSxCvABcDVwXUSMJ51xbCPpdUmvSDqisNr5wNrA68CzwJ+B04BjI2JOW8RtZmbzassbAMcDPYDNgQ8k9QReBW4EVgV2Bg6U9AOAiJgYEZtHxICI+D7pLKM38LKkuyU9IGmrlnYmaX9JYyWNnTx5cp0Pzcw6k4ho7xBaVdXjabPEERGz8+N+UvXTjyJii4i4KiJmRcTTwHnATrXrShJwJnBYfr4E2BW4QlKzxxARoyJiaEQMHTBgQL0Oy8w6me7duzNlypRFJnlEBFOmTKF79+6l16l7G4ekbYCfR8TXC8XdgWGSfgUcHZ/8BZYAZjSzmT2BpyPiWUlDgKci4s2cUAYAb9XxEMzM/m3gwIFMmDCBRakmo3v37gwcOLD08m1xOe4TwHqS9gSuA7YBdgS+DVwFTJL0a+ArwEHAAcWVJfUAjgS2yEXjgA0lzSFVfb3TBsdgZgZAt27dWGmlldo7jHbVFldVvSVpB+Ac4ELSF/8eEXGbpG8BZwEnAP8inX38b80mDgOuiohJefpY4L+BnsDBEfFRvY/BzMw+0SY3AEbEfcD6zZQ/Amy6gHVPqJl+AvhiqwZoZmaluVt1MzOrxInDzMwqceIwM7NKnDjMzKwSJw4zM6vEicPMzCpx4jAzs0qcOMzMrBInDjMzq8SJw8zMKnHiMDOzSpw4zMysEicOMzOrxInDzMwqceIwM7NKnDjMzKwSJw4zM6vEicPMzCpx4jAzs0qcOMzMrBInDjMzq8SJw8zMKnHiMDOzSpw4zMysEicOMzOrxInDzMwqceIwM7NKnDjMzKwSJw4zM6vEicPMzCpx4jAzs0qcOMzMrJI2SRySvi3peUnvSXpE0oa5vJ+kWyRNl/S6pAMK66wg6XFJ70q6WlLXXN5b0tOS+rRF7GZm9ml1TxySVgOuBPaLiL7AccDtkhYHLgI+BJYBtgNOk7RJXvXHwGigPzAY+EYuPwq4MCKm1Tt2MzOb12L13kFEvCxp+Yh4N581DAD6AcsBOwGrR8QM4GlJ1wO7Aw8Ds4HeOcauwFxJg4GtgI3rHbeZmTWvTaqqctJYBfgAuBq4DlgamBMRrxYWfRYYkl+fD6wNvJ7L/wycBhwbEXPaIm4zM5tXWzaOjwd6AJuTEkgfYGbNMjOAXgARMTEiNo+IARHxfdJZRm/gZUl3S3pA0lYt7UzS/pLGSho7efLkehyPmVmn1GaJIyJm58f9pOqnjUmJpKgnKXl8iiQBZwKH5edLgF2BKyQ1ewwRMSoihkbE0AEDBrTikZiZdW5t0Ti+jaTRNcXdgWWBxSStXCgfAjzfzGb2BJ6OiKaqrKci4k1ApDYTMzNrI21xxvEEsJ6kPSV1kbQdsCNwK/BH4ARJS+RLdHcHrimuLKkHcCTpaiyAccCGOeH0AN5pg2MwM7OsLa6qekvSDsA5wIWkL/49ImKMpOeAy4GppARwWEQ8UrOJw4CrImJSnj4W+G9StdbBEfFRvY/BzMw+UffEARAR9wHrN1M+Cdh2AeueUDP9BPDFVg3QzMxKc5cjZmZWiROHmZlV4sRhZmaVOHGYmVklThxmZlaJE4eZmVXixGFmZpU4cZiZWSVOHGZmVokTh5mZVeLEYWZmlZROHJK+lp+Xl3SxpOMkLVG/0MzMrBGVShySTgIuy5O/BdYANgXOq1NcZmbWoMr2jrsvsK6kzwHfAlYmdYX+CnBAnWIzM7MGVLaqqhfwHrA1MC4iXgNmA93qFZiZmTWmsmccj5GqpTYCbpD0eeBsoHbQJTMzW8SVPeP4T9JZxyPAKcCKwOeA79cpLjMza1Blzzh+DvwwIqbl6ceB7eoTkpmZNbKyZxzfBWbUMxAzM+sYyp5xXAf8UdJ1wJTijIi4vdWjMjOzhlU2ceyYn0+sKQ/SpblmZtZJlEocEbFSvQMxM7OOoUqXI1tKulLSnZL6SzpKkuoZnJmZNZ6yXY58H7gYeAbYBJgL7AycWr/QzMysEZU94zgK+FZEnAnMjYipwA7AyLpFZmZmDals4ugLjMuvIz+/jbscMTPrdMomjkeAUyQVl/8p8Gjrh2RmZo2s7OW4PwL+COwH9JE0nnRD4Lb1CszMzBpT2ctxx0tam9TJ4SBgIvBoRMyuZ3BmZtZ4qgwd+yVSx4YzSG0e35C0TV2iMjOzhlXqjEPSOcBBwJukcTia+M5xM7NOpmwbx38Aa0fE8/UMxszMGl/ZqqopwBufdSeShkt6TNI0SS9JGpnL15I0R9L0wuPoPK+PpHskvSfpDkm9cnkXSY9KWvGzxmNmZp9d2TOOg4H/kXQp8G5xxoJ6x5W0HHAraTCom0kN7HdKegn4IvCXiPhmM6vuBUwGlgKuydMXka7sGh0R40vGbmZmrahs4tgG2AxYFZhTKC/TxjEYuCkibsrTD0t6BNiUdIXWUy2sNxtYElg8xzlXUh9SW8tmJeM2M7NWVraqaiSwRkSsEBErFR4LbBiPiIcj4j+bpiUNADYAngTWBdaT9E9Jr0s6XdLiedFrga7ABNId6lcDxwAXFEYibJGk/SWNlTR28uTJJQ/TzMwWpGzieDs/Foqk/sBtpKqm0aS2kzuBrwDDga3IY35ExPSI2C4i+kfETsAywJbArZJukfSwpD1b2ldEjIqIoRExdMCAAQsbupmZZWWrqi4mtUtcArxF6h0XKD8CoKRVgNuB54A98ro7FxZ5RdIvgV8CRzaziTNIZxyHAw8BFwDPSbozIhY6qZmZWTllE8cP8/OxNeWl7uOQtDHpTOMy4KiImCupb97eiRHxfl50CZoZ21zSV4EeEXGXpEOAOyJipqSJwCq0wtmQmZmVU/cRACUNAv4EHBcRvynMmkYakrarpCNIDeXHkK6cKq4v0tlGUzvJOGBDSWOB1ViIy4TNzKy6KiMAbiXpWkn3S7pJ0k4lV/0hsDRwavF+DdIYH9sBXyZddnsPcCWpCqpoJPBkRDybp08DdgFeBM6OiAllj8HMzBZe2S5H9gR+DYwC7iNVD10qqWdEXDO/dSPiaODo+SzS3D0cxfWvJCWUpulxwHpl4jYzs9ZXto3jKGC7iHi4qUDSTcDlpJvzzMyskyhbVbU88FhN2RPAF1o3HDMza3RlE8dTpK4+in5Ay3d9m5nZIqpsVdVPgTsk/RAYD6xEuiFv63oFZmZmjans5bhPSvoSqc+qgcANwJ0RMbWewZmZWeMpe1XVtRGxB3BdTfkfI2K7ukRmZmYNqcXEIWkw8KM8uYuk2hvt+gAb1ycsMzNrVC0mjogYJ6kL0I/UiF7bU+As0s15ZmbWicy3qioifg4g6W8RcX7bhGRmZo2s7OW4V0r6LwBJwyS9IGm0pM/ch5WZmXVMZS/HvRRYPHc4eAFwN6mTwotZQJchZma2aCmbOLYg3SW+IrA2adCl93F35mZmnU7ZqqouQC9ge2BsRLwHfB74uF6BmZlZYyp7xnEL8BdgBeBoSUOAm/LDzMw6kSojAO4DTIuI6/MwsJcD59YtMjMza0hluxz5mDQWR9P0K8CZ9QrKzMwaV9kuR+aSxhefR0R0bdWIzMysoZWtqlqr8FrAcsDBuI3DzKzTKVtV9WxN0T8kPQY8CVzR2kGZmVnjKns5bnOWYt7+q8zMbBFXto3jRj7dxtEN2AD4fT2CMjOzxlW2jeMfNdMfA9cDf2jdcMzMrNGVbeM4Id/093pETJc0FJgeEbPrG56ZmTWaUm0ckvYAHgNWyUXrAg9I2qlegZmZWWMq2zh+HLBFRDwNEBGXACOAU+oVmJmZNaayiWM54Kmasn8Ay7RuOGZm1ujKJo6xwOF5PA7y88+Av9UrMDMza0xlr6o6ELgDOETSv4DlgSnAdvUKzMzMGlPZq6qeyz3ifpU0DscE4JGImFPP4MzMrPGUPeMgX3r71zrGYmZmHcDCdDliZmadUJskDknDJT0maZqklySNzOXdJV0m6T1Jb0k6rrBOH0n35Hl3SOqVy7tIelTSim0Ru5mZfdp8E4eksfn5kM+6A0nLAbcCpwOfA/4DOF/SxsCJpJsKBwMbAvtK+l5edS9gMqkzxXfyNMB+wOiIGP9ZYzIzs89uQW0cQyRtBpws6S7SWByfEhHPLWAbg4GbIqJp7I6HJT0CbEpKBntExDvAO5IuAvYg9YM1G1gSWDzHOVdSH+AgYLMyB2dmZq1vQYnjGlKDeBegdkwOSD3mzncEwIh4GHi4aVrSAFLPuqOAZYFi4nmWdEYBcC2wPekKrvuBq0l3sF8QEdMWELeZmdXJfKuqIuKAiOgGzIyILs08Kg0bK6k/cBswmtT3FcDMwiIzgF5539MjYruI6B8RO5HuUt8SuFXSLZIelrTnfPa1v6SxksZOnjy5SphmZjYfZRvH+0paQtJWkv5T0raSelTZUb4P5EHgX6TqqBl5VnE7PQvltc4AjgEOBx4iJZGTczKaR0SMioihETF0wACPN2Vm1lrKJo6VSNVIl5Oqki4F/ilp9TIr54bwR4D/AXaJiFkRMZXU+L1GYdEhwPPNrP9VoEdE3JWXeSoiZgIT+aTHXjMzawNlE8f5pDaHQRGxCfAF4GLgrAWtKGkQ8CfghIg4IiLmFmZfBxwtqbek1UgN31fXrC/S2cZhuWgcsKGkpYDVgDdKHoOZmbWCsoljI+CkiAiA/OX/K2CTEuv+EFgaOFXS9MLjaOBo4C3Sl/9DwCURcUPN+iOBJyOiqXH+NGAX4EXg7IiYUPIYzMysFZTtcmQq6dd9sRppddL9FfMVEU0JoiV7LGD9K4ErC9PjgPUWtF8zM6uPsonjPOAOSecC40ltHocAZ9YrMDMza0xle8c9X9JU0l3fA0n3VhwTEVfPf00zM1vUVOkd91pSA7mZmXVi7h3XzMwqceIwM7NKnDjMzKyS0olDUj9J+0g6RlJPScPqGZiZmTWmUokjJ4nnSDfeHUkaI+M2SXvNd0UzM1vklD3jOA8YGRHbAbPz3dpbA8fWLTIzM2tIZRPHasDd+XVTtyOPk8bTMDOzTqRs4ngZ2KlYIGlELjczs06k7A2AhwF/lLQ30EPSlaTR+b5dr8DMzKwxle1y5K+ShgC7A6+TxsHYICJeqWdwZmbWeKp0OfIacGodYzEzsw6g7OW4cyXNaebxoaQJki6V1LfewZqZWfsr2zh+KPA4sA1pqNdvAg+QLtP9IanH3PPrEaCZmTWWslVVBwKbRsTkPP2ipKeAhyLicEn3A/+sS4RmZtZQyp5xfJ58/0bNur3z6xmAWisoMzNrXGUTxw3A7ZJ2kLSupB2AW4FrJH0OuAy4r15BmplZ4yhbVXUQcDhwBjCINALgFcDpwLrANODgOsRnZmYNpux9HB8Dv8yPWo/nh5mZdQKlEoekAcCPgeWArrm4G/CViFi7TrGZmVkDKltVdSXQD5gM9AdeBXYkdUViZmadSNnG8c1I93AcC7wdEbsD3wU2rFdgZmbWmMqecXwUEVMkfQCslcv+CFxen7DMzKxRlT3jeFLSD4APgXclfQVYnXnv7TAzs0Vc2TOOQ4CbgD8DvwIeJd3wd3Kd4jIzswZV9nLcZ4EhefIVSQ8BPXA3I2ZmnU7Z3nFfK05HxGsR8QLwVl2iMjOzhtXiGYeklWnuT4oAAA/oSURBVIH/JlVJLS/psZpFegPv1DE2MzNrQC0mjoh4VdLJpPs3LgIuqFlkFqlrdTMz60Tm28YREbcBSHoqIp5qm5DMzKyRlb0c9y1JF0r6s6TRxUeVnUnaRNK/CtNr5ZEEpxceR+d5fSTdI+k9SXdI6pXLu0h6VNKKVfZtZmato+zluJcB3Uk3/X34WXYk6bvAb4HZheJ1gb9ExDebWWUvUhcnSwHX5OmLgP2A0REx/rPEYWZmC6ds4hgGLBMRnzVpHA/sABxP6rakyXpAS1Vgs4ElgcVznHMl9SF18b7ZZ4nDzMwWXtmqqleAFRZiP6MiorkksS6wnqR/Snpd0umSFs/zriX1xDuB1BPv1cAxwAURMW0hYjEzs4VQ9ozjZuBOSZcC7xZnRMSFC1o5Iia2MGsK8CBwMbAs6e70ucCRETEd2K5pQUkrAVsC50i6JS9/QURc09yGJe0P7A+wwgoLk/PMzKyobOLYEhgPbFVTHsACE0dLImLnwuQrkpoGizqymcXPIJ1xHA48RLo8+DlJd0bE281sexQwCmDo0KHuU8vMrJWU7XJki9besaS+pPaOEyPi/Vy8BDCjmWW/CvSIiLskHQLcEREzJU0EVgHmSRxmZlYfZds4kPSfku6V9Lyk5ST9VtKSC7HvaaTBoE6Q1C3fqX4MNV21SxLpbKNp0KhxwIaSlgJWA95YiBjMzKyisn1VHUu6DPZ80vCx7wMD8/RnEhFBasP4Mumy23tIIw3W3qE+Engyd7QIcBqwC/AicHZETPisMZiZWXVl2zh+CKwfEW9KGhUR0yXtAbxUZWcRMYY09GzT9ItAc/dwFNe5kpRQmqbHkS7jNTOzdlC2qmox4L38Wvl5Fh7Iycys0ymbOEYDl0rqB4SkxUhVRvfULTIzM2tIZRPHgcDSwCTgc6Qrn9YADq5TXGZm1qDKXo47FfiWpGWBQcCbEfF6XSMzM7OGVPaqqpUlPQwsHxGPAwdKekDSwPqGZ2ZmjaZsVdXFwH3A83n6JOAvudzMzDqRspfjbgh8M997QUTMkHQiqa8pMzPrRMqecUxh3nsn1iI1lpuZWSdS9ozjDOAuSVcBr5MayPfkk25ArJN47cS12juEhrHCL55p7xDM2kXZq6ouljQO2A0YQhojY9eIuK+OsZmZVeIfNp+o5w+bUolD0q3AnhFxZ90iMTOzDqFsG8f6wJx6BmJmZh1D2TaOW4GHJP2BmiupyowAaGZmi46yiWMN4B3g6zXlCzUCoJmZdTztNgKgmZl1TO05AqCZmXVA7TYCoJmZdUxtOgKgmZl1fB4B0MzMKvEIgGZmVolHADQzs0o+6wiAEyPijbpGZmZmDals4ziSvg7sDiwLjJf0u4j4W90iMzOzhlT2ctyDgBtJl+GOBmYDd0vatY6xmZlZAyp7xvFTYMuIeKqpQNI1wNXATfUIzMzMGlPZxvG+wAs1ZU8DS7VuOGZm1ujKJo5fA7+T9HkASb1IowJeIWlJST0k9ahXkGZm1jjKVlUdSjrr+J6k6UAvPrkR8Of5dQBdWz1CMzNrKGUTxzp1jcLMzDqMsvdxjK93IGZm1jGU7lbdzMwMnDjMzKyiNk0ckjaR9K/CdHdJl0l6T9Jbko4rzOsj6Z487458JReSukh6VNKKbRm7mZklbZY4JH0XuAPoVig+EVgFGAxsCOwr6Xt53l7AZNK9Iu/kaUgDSo12u4uZWftok8Qh6XjgCOD4mll7ASdExDs5EVwE7JHnzQaWBBYnNeLPldQHOAj4VRuEbWZmzWirM45REbEeUOyyZClSh4nPFZZ7FhiSX19Lui9kAuks5WrgGOCCiJi2oB1K2l/SWEljJ0+e3DpHYWZmbZM4ImJiM8W98vPMQtmMpvKImB4R20VE/4jYCVgG2BK4VdItkh6WtOd89jkqIoZGxNABAwa00pGYmVl7XlU1Iz8XuyrpWSivdQbpjONw4CFSEjlZUv+6RWhmZvNot8SRB4eaTBpJsMkQ4PnaZSV9FegREXflZZ6KiJnARFLjupmZtZH2vo/jOuBoSb0lrUZq+L66uIAkkc42DstF44ANcxvJaoBHIjQza0PtnTiOBt4iffk/BFwSETfULDMSeDIins3TpwG7AC8CZ0fEhLYK1szMKgwd2xoiYgzQvzA9k08uv21pnSuBKwvT44D16hOhmZktSHufcZiZWQfjxGFmZpU4cZiZWSVOHGZmVokTh5mZVeLEYWZmlThxmJlZJU4cZmZWiROHmZlV4sRhZmaVOHGYmVklThxmZlaJE4eZmVXixGFmZpU4cZiZWSVOHGZmVokTh5mZVeLEYWZmlThxmJlZJU4cZmZWiROHmZlV4sRhZmaVOHGYmVklThxmZlaJE4eZmVXixGFmZpU4cZiZWSVOHGZmVokTh5mZVeLEYWZmlThxmJlZJe2eOCT9WNJHkqYXHl+VtI6k5yVNlXR6YfkVJD0mqWt7xm1m1lm1e+IA1gWOi4hehcf9wDHAecAXgF0kDcnLnwr8V0TMaad4zcw6tUZIHOsBTzVTPhvoA3TNj7mSNgb6RsRdbRifmZkVtGvikLQ4sAawn6SJuWrqP/Lsk4HvAa8AVwEvAWcCh7dLsGZmBsBi7bz/ZYGHgN+RksQmwG2S3oqIO0nVWABI2h14BlhS0n3AHOBnEfG35jYsaX9gf4AVVlihrgdhZtaZtGviiIjXgOGFovskXQ3sBNzZVChpSeAoYMtcfgAwFxgFDG1h26PyfIYOHRp1CN/MrFNq76qqdSQdUVO8BDCjpuznwDURMQkYQmoTeQb4cv2jNDOzovauqnofOF7SK8DNpLOP71A4C5G0HPBdYP1cNA7YkNRg/nrbhWpmZtD+VVWvSNoFOAW4DPg/YPeIeLKw2CnASRExK08fClxPuurqB20Zr5mZtf8ZBxFxO3D7fObvUzN9JzCo3nGZmVnzGuE+DjMz60CcOMzMrBInDjMzq8SJw8zMKnHiMDOzSpw4zMysEicOMzOrxInDzMwqceIwM7NKnDjMzKwSJw4zM6vEicPMzCpx4jAzs0qcOMzMrBInDjMzq8SJw8zMKnHiMDOzSpw4zMysEicOMzOrxInDzMwqceIwM7NKnDjMzKwSJw4zM6vEicPMzCpx4jAzs0qcOMzMrBInDjMzq8SJw8zMKnHiMDOzSpw4zMysEicOMzOrxInDzMwqaffEIWmwpL9KmiHpZUn/L5evI+l5SVMlnV5YfgVJj0nq2n5Rm5l1Xu2eOIAbgLHA0sCPgOskrQgcA5wHfAHYRdKQvPypwH9FxJz2CNbMrLNbrD13Lml1YB1geETMAv4s6X5gV2A20Afomh9zJW0M9I2Iu9orZjOzzq69zzjWAP4vIj4olD0LDAFOBr4HvAJcBbwEnAkc3tZBmpnZJ9r1jAPoBcysKZsBfCEingXWbSqUtDvwDLCkpPuAOcDPIuJvzW1Y0v7A/nlyuqQXWzv4zmhF6A+83d5xNITj1N4RWA1/Pgta5/O5YnOF7Z04ZgA9asp65vJ/k7QkcBSwJXAncAAwFxgFDG1uwxExKs+3ViRpbEQ0+56btTd/PttGeyeOF4DBkpYsVFcNAUbXLPdz4JqImJQbyZ8CBHy57UI1MzNo5zaOiHgOeB74haSukrYFNgdubFpG0nLAd4Fzc9E4YENgY+D1Ng3YzMza/YwD4DvA5cD7wERg94gYX5h/CnBSvuoK4FDgetJVVz9oy0ANcPWfNTZ/PtuAIqK9YzAzsw6kvS/HNTOzDsaJYxEnKSTNlDS95rFBG8ewZlvtzxYN+XMzXVLPmvJukt6WNK7Cdvz5a0VOHJ3DhhHRq+bxeHsHZVbCh8AONWVb0xjts52WE0cnJ2kLSU9Imibpb5I2zeWD86+6Y/PzZEn7SDo6v34z35TZtJ3vS3ox/0L8l6RftLC/r0i6T9K7uRPLHdvqWK1DugnYraZsD+APxQJ//tpYRPixCD+AANZsYd4KpDv3v03qD+xbwCRgADA4r/tboBuwL/Ax8GvSr739gcl5O5sAbwKrku6vGU66s3/VYgykngImAYfkbWwAvAWs0d7vkx+N98ifmy1INwQvnct6AxOAnYFxucyfvzZ++Iyjc3go/8JqepyUy/cARkfEjRExJyLuAB7n01UDZ0bEx8C9pH+2MyJiNukmzf6SegBPk6rD/gl8Pi/3AbBcTRzbA5Mi4tyImB2puqy5X5RmTSYD9wG75OmdgT8BHxWW8eevjbmesHMYFhH/aKZ8ELCVpHcLZd2ARwrTb+Xnpm7sm5adnZ+7kLp/OVLSDsB7wGOkX361Y6YMAlav2d9ipPtyzFpyHbAPcAnpx85JQN+aZfz5a0NOHJ3bROD3EbFXU4GkQaTk0C8XzS2xnZ+Sun8ZEhHv577FRrawv0ci4quF/S1HagA1a8ktwIWS1gNWBh4Ati3M9+evjbmqqnO7Hthe0lZKNiCd9n+l4nZ6k85AZkvqQ+oepgvp7KXoT6RffHvmLmZWAx4Ctlmoo7BFWkRMJ312rgBuiNxwUeDPXxtz4ujEcp3wt0mjKr5HqhL4aUQ8UHFTZ5HOTCaROqB8ltSL8acSUES8Q2qA3x+YQmonuTgirl2Iw7DO4VpgLeCaZub589fG3OWImZlV4jMOMzOrxInDzMwqceIwM7NKnDjMzKwSJw4zM6vEicPMzCpx4jBrMJJ65TEkBrd3LGbNceIwM7NKnDjMKpL0NUnPSnpP0k2SbpR0vKQeki6U9EYew+TXkhbP61wh6UxJ9+YxIx6WtEZhmwdKGidpInB4zf5aHEMin5mcLmmqpE+tZ1YvThxmFeS+kG4GzgT6A7cBu+bZZwFDgHWA1Ugd8h1XWH1f4EhgKeD/gLPzNkcAJwD/D1gFGFjYXy/gz3mf/Umd9/22mHSApUndiV/Uekdq1jInDrNqtgMmRMTlEfFxRFwJPErqxntv4LCImJz7RTqRT/fSenNEPJzHN7mZlFwgjQdxTUQ8HREfkJJLkzJjSFyd571fh+M1m4e7VTerZhDwWk3ZONKoid2BeyQ1dQAnoJukpnEhiuNAfMgn40UsD9zTNCMiJkmaUdjfgsaQmPLZDsXss3HiMKtmAunLvGgg8DJpVLr1IuIVgDwuxLIRMUfS/Lb5Oqlai7xeH6BnnvQYEtZwXFVlVs1twEBJI/OYDt8BNiWNkHgtcLqkpXLSOA8YVWKblwHflbSupCWA0wrzPIaENRwnDrMKImIaaUyHnwBvk8YzeYx0tvET0pgQ/yCdKfQH9iyxzUeAg4EbSUP1TgJm5HkeQ8IajsfjMKtAUj9g5dxI3VT2N9KXeZmzC7MOz2ccZtUsAdwraZ083O62pPGuR7dzXGZtxo3jZhVExERJB5AuiV0WGA/smYfhNesUXFVlZmaVuKrKzMwqceIwM7NKnDjMzKwSJw4zM6vEicPMzCpx4jAzs0r+P8XcDVv/BF+HAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 计算老少，男女的用户流失比例：\n",
    "def barplot_percentages(feature,orient='v',axis_name=\"percentage of customers\"):\n",
    "    ratios = pd.DataFrame()\n",
    "    g = (data.groupby(feature)[\"Churn\"].value_counts()/len(data)).to_frame()\n",
    "    g.rename(columns={\"Churn\":axis_name},inplace=True)\n",
    "    g.reset_index(inplace=True)\n",
    "\n",
    "    print(g)\n",
    "    if orient == 'v':\n",
    "        ax = sns.barplot(x=feature, y= axis_name, hue='Churn', data=g, orient=orient)\n",
    "        ax.set_yticklabels(['{:,.0%}'.format(y) for y in ax.get_yticks()])\n",
    "        plt.rcParams.update({'font.size': 13})\n",
    "        plt.legend(fontsize=10)\n",
    "    else:\n",
    "        ax = sns.barplot(x= axis_name, y=feature, hue='Churn', data=g, orient=orient)\n",
    "        ax.set_xticklabels(['{:,.0%}'.format(x) for x in ax.get_xticks()])\n",
    "        plt.legend(fontsize=10)\n",
    "    plt.title('Churn(Yes/No) Ratio as {0}'.format(feature))\n",
    "    plt.show()\n",
    "barplot_percentages(\"SeniorCitizen\")\n",
    "barplot_percentages(\"gender\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 518.4x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 按照老年和青年划分下，查看男女流失比例。\n",
    "data['churn_rate'] = data['Churn'].replace(\"No\", 0).replace(\"Yes\", 1)\n",
    "g = sns.FacetGrid(data, col=\"SeniorCitizen\", height=4, aspect=.9)\n",
    "ax = g.map(sns.barplot, \"gender\", \"churn_rate\", palette = \"Blues_d\", order= ['Female', 'Male'])\n",
    "plt.rcParams.update({'font.size': 13})\n",
    "plt.show()\n",
    "\n",
    "# 小结：\n",
    "\n",
    "# 用户流失与性别基本无关；\n",
    "# 年老用户流失占显著高于年轻用户。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Dependents Churn  percentage of customers\n",
      "0         No    No                 0.481329\n",
      "1         No   Yes                 0.219083\n",
      "2        Yes    No                 0.253301\n",
      "3        Yes   Yes                 0.046287\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axis = plt.subplots(1, 2, figsize=(15,4))\n",
    "axis[0].set_title(\"Has Partner\")\n",
    "axis[1].set_title(\"Has Dependents\")\n",
    "axis_y = \"percentage of customers\"\n",
    "\n",
    "# Plot Partner column\n",
    "gp_partner = (data.groupby('Partner')[\"Churn\"].value_counts()/len(data)).to_frame()\n",
    "gp_partner.rename(columns={\"Churn\": axis_y}, inplace=True)\n",
    "gp_partner.reset_index(inplace=True)\n",
    "ax1 = sns.barplot(x='Partner', y= axis_y, hue='Churn', data=gp_partner, ax=axis[0])\n",
    "ax1.legend(fontsize=10)\n",
    "ax1.set_xlabel('伴侣')\n",
    "\n",
    "\n",
    "# Plot Dependents column\n",
    "gp_dep = (data.groupby('Dependents')[\"Churn\"].value_counts()/len(data)).to_frame()\n",
    "# print(gp_dep)\n",
    "gp_dep.rename(columns={\"Churn\": axis_y} , inplace=True)\n",
    "# print(gp_dep)\n",
    "gp_dep.reset_index(inplace=True)\n",
    "print(gp_dep)\n",
    "\n",
    "ax2 = sns.barplot(x='Dependents', y= axis_y, hue='Churn', data=gp_dep, ax=axis[1])\n",
    "ax2.set_xlabel('家属')\n",
    "\n",
    "# 解决中文显示问题\n",
    "plt.rcParams['font.family'] = ['sans-serif']\n",
    "plt.rcParams['font.sans-serif'] = ['Arial Unicode MS']\n",
    "#设置字体大小\n",
    "plt.rcParams.update({'font.size': 15})\n",
    "ax2.legend(fontsize=15)\n",
    "\n",
    "#设置\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Kernel density estimaton核密度估计\n",
    "def kdeplot(feature,xlabel):\n",
    "    plt.figure(figsize=(9, 4))\n",
    "    plt.title(\"KDE for {0}\".format(feature))\n",
    "    ax0 = sns.kdeplot(data[data['Churn'] == 'No'][feature].dropna(), color= 'navy', label= 'Churn: No', shade='True')\n",
    "    ax1 = sns.kdeplot(data[data['Churn'] == 'Yes'][feature].dropna(), color= 'orange', label= 'Churn: Yes',shade='True')\n",
    "    plt.xlabel(xlabel)\n",
    "    #设置字体大小\n",
    "    plt.rcParams.update({'font.size': 15})\n",
    "    plt.legend(fontsize=10)\n",
    "kdeplot('tenure','tenure')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  InternetService Churn  percentage of customers\n",
      "0             DSL    No                 0.278574\n",
      "1             DSL   Yes                 0.065171\n",
      "2     Fiber optic    No                 0.255431\n",
      "3     Fiber optic   Yes                 0.184154\n",
      "4              No    No                 0.200625\n",
      "5              No   Yes                 0.016044\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 按照多线特征绘制：\n",
    "\n",
    "plt.figure(figsize=(9, 4.5))\n",
    "barplot_percentages(\"InternetService\", orient=\"h\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      MultipleLines Churn  percentage of customers\n",
      "0                No    No                 0.360784\n",
      "1                No   Yes                 0.120545\n",
      "2  No phone service    No                 0.072696\n",
      "3  No phone service   Yes                 0.024137\n",
      "4               Yes    No                 0.301150\n",
      "5               Yes   Yes                 0.120687\n"
     ]
    },
    {
     "data": {
      "image/png": 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u6Z0q6WagbTzrg6R/rr5DGhfcpX/OrIdo9mXjnjx5mncT6UbWp5DGwE0jdfPeAYypse54at/W5y/l+aQvrMdJV9U+SEqATmPO2//UKu98atyOpDyf9I/vq8BxnRzjxFr7Ka2zCqlF7iVSUvAw6UusT2GdhYGfkr7YppFuafJbPri9y7nApTXK3p3SrXFKy7fOy8eX5g8hXRjzMqkb8n5gtxrbD8nb79jJMY6mdLuZGuvslde5Kb9fiXTF7SukK2v/nMvZkcIte0i3cLk0nz9TSRdfHUbptjik5Pt00ljH/5K6q38ALNBJ7P1Jt915Idf9HaRhGevnfXw7r6f8e3uAdNHHi6RxncO7WDe7F+YNICV6d5bWne32P3neJ0m3RHqHdPHSj0n3uGz39j953kGkfz6mkR6/OFschd/tsfn8ejPX77kUbntF6fY/hfk7ku6ROi3v5wbSVfjlz3V0MH2+8DkaX9qu1ud3jvnAJ/Lv4dV8Ht0L7FpaZ2lSV/a/cj1OJD0DfpnyPjz17En5F2pm1uNJOoH0ZTk4eukfL0n7k5KxIdHJRT7WWpSeivMMcHhEHNbUYMy6yGMkzayVnEpqVd282YE0Qx7P+k1Sd7eTSDNrOieSZtYyIuIZ0uP5Dmp2LE3yJVKXe5WxrGZmDedE0sxazVhgLUmjmh3IvJSvmP4Z6T6fbzc7HjMzwGMkzczMzKwat0iamZmZWSW+j6Q13MCBA2PIkCHNDsPMzMwa4L777nslImo+r92JpDXckCFDuPfeeztf0czMzHo8Se0+0tRd22ZmZmZWiRNJMzMzM6vEiaSZmZmZVeJE0szMzMwqcSJpZmZmZpU4kTQzMzOzSnz7H2u4RydPYb39L2x2GGZmZr3CfceOadq+3SJpZmZmZpU4kTQzMzOzSpxImpmZmVklTiTNzMzMrBInkmZmZmZWiRNJMzMzM6vEiaSZmZmZVeJE0szMzMwqcSJpZmZmZpU4kTQzMzOzSpxImpmZmVklTiTNzMzMrBInkmZmZmZWiRNJMzMzM6vEiaSZmZmZVeJE0szMzMwqcSJpZmZmZpU4kTQzMzOzSpxImpmZmVklTiTNzMzMrBInkmZmZmZWiRNJMzMzM6vEiaSZmZmZVeJE0szMzMwqcSJpZmZmZpU4kTQzMzOzSpxI9jKSJkp6QtIiNZaNl3RWM+IyMzOz1uNEsndaDfh5s4MwMzOz1uZEsnd6GthP0ohmB2JmZmaty4lk7/Rr4E7gXEkL11pB0uqSrpU0NU8XSBo4b8M0MzOznsyJZO/0HrAn8BHg0PJCSR8B7gZeAUYC2wPDgL9IWnAexmlmZmY9mBPJXioiHgcOA/aXtG5p8T6kJHKviHgsIv4O7AysA2w7TwM1MzOzHsutS73bccCOwHmS1i/MHwbcHRGz2mZExFOSXgDWBK4pFyRpb2BvgEFL9eWqJY7t1sDN2rPyIQ81OwQzs17DLZK9WES8C+wBrA6MLS+usckCwLvtlHV2RKwfEet/aLEFGhuomZmZ9UhOJHu5iHgYOBI4GFglz34E2EhS37b1JK0OLANMmOdBmpmZWY/kRNIAfkFKHlfM788EBpKu6l5d0obARcCDwJ+bE6KZmZn1NE4kjYiYSerinpXfPwVsBHwI+DtwJXAXsHlx3KSZmZn1br7YppeJiCHtzP8H0LfwfgLptj9mZmZmNblF0szMzMwqcSJpZmZmZpU4kTQzMzOzSpxImpmZmVklTiTNzMzMrBInkmZmZmZWiRNJMzMzM6vEiaSZmZmZVeJE0szMzMwqcSJpZmZmZpU4kTQzMzOzSpxImpmZmVklTiTNzMzMrBInkmZmZmZWiRNJMzMzM6vEiaSZmZmZVeJE0szMzMwqcSJpZmZmZpU4kTQzMzOzSpxImpmZmVklTiTNzMzMrBInkmZmZmZWiRNJMzMzM6vEiaSZmZmZVeJE0szMzMwqcSJpZmZmZpU4kTQzMzOzShZsdgA2/1lo+bVY+ZB7mx2GmZmZdTO3SJqZmZlZJU4kzczMzKwSJ5JmZmZmVokTSTMzMzOrxImkmZmZmVXiRNLMzMzMKqmcSEpaRNJ6khZtZEBmZmZm1hq6nEhKWkHSnyRtIGkpYAJwD/CMpHW6LUIzMzMz65HqaZE8Lf98HtgN6A+MAC4BjmtwXGZmZmbWw9XzZJvNgI0iYrKkbYE/RsQ9kl4CHuie8MzMzMysp6qnRXJBYFYeE7kZcEuevwQwq9GBmZmZmVnPVk+L5N+Bo4BX83bXSFodOBG4vRtiMzMzM7MerJ5E8rvApcBg4AcR8bykA4CVge26IzgzMzMz67m6nEhGxBPAJ0qzD4+I/RobkpmZmZm1grruIylpVUm/lHSlpGWAHSSt202xmZmZmVkP1uUWSUkbAX8C/gZsASwCrAecLWmHiPhL94RorebRyVNYb/8Lmx1Gr3XfsWOaHYKZmfUS9bRI/pLUlb0DMB0gIr4PHAEc2Q2xmZmZmVkPVk8iuQFwRY35FwJrNSYcMzMzM2sV9SSSbwAr1Zi/OvBWY8IxMzMzs1ZRTyJ5GXCGpOFAAAtJGg2cCfy+G2IzMzMzsx6snkTyAOBe4H5gaeBx4Kb8/oDGh2ZmZmZmPVk995GcDnxD0uHAsLztIxHxVHcFZ2ZmZmY9Vz1PtgEgIp4Fnu2GWMzMzMyshXS5a1vSapJuljRV0tuSphWn7gzSzMzMzHqeelokTweWJd0z8pXuCcfMzMzMWkU9ieQIYNOIeKi7gjEzMzOz1lHPVdtTgXe7KxAzMzMzay31JJJHAcdJWqG7gjEzMzOz1lFP1/YPgKHAc5JmUWqdjIhFGxmYmZmZmfVs9SSSR3dbFGZmZmbWcuq5IfkF3RmImZmZmbWWDhNJSWcAP4yId/LrdkXEvg2NzMzMzMx6tM5aJNcorLNGN8diZmZmZi2kw0QyIjav9drMzMzMrJ7b/9QkaUNJf2tEMGZmZmbWOuY6kQT6A5s0oBwzMzMzayGNSCTNzMzMrBdyImlmZmZmlTiRNDMzM7NKOruP5AFdKONjDYrFzMzMzFpIZ/eR3KeL5Tw7t4GYmZmZWWvp7D6SQ+dVIGZmZmbWWuoeIylpDUmfk7SIpJXr2G6ipCckLVJj2XhJZ9UbSx37PkzSY91VfqtwPZiZmVkjdTmRlLSkpBuBCcDvgWWA0yTdI2mZLhazGvDz+sO0BjkG2KjZQZiZmdn8oZ4WyWOAWcCHgTfzvH2Bt4Hju1jG08B+kkbUsV9rkIiYFhGvNTsOMzMzmz/Uk0h+ATgoIl5pmxERk4HvAtt2sYxfA3cC50pauL2VJK0u6VpJU/N0gaSBHaw/UdKBkm6RNF3Sk5K+Ulqtj6RDJT0v6W1JV0tarlDGIEm/k/SCpDclXSVpaGkfP5B0jaRpkv4l6celODaXdHeO4d+Sju7kOMdIeiyvP1HSwV0tT9IQSSHpJ5Jey+WcJ+nWUhkbSnpP0tBy17akFSRdnLefKulSSSsUlu8k6WFJMyQ9LekASWrveMzMzKx3qSeRXBx4q8b8mcAc4x7b8R6wJ/AR4NBaK0j6CHA38AowEtgeGAb8RVJHFwcdClwFrEVKWC+WtH5h+WrAJ4DNga2BEcAReZ9LA3cBSwNbAZsCiwG35mXFffyedMujXwFHSdo8l7EqcD1wDbAGsBPwaeC0do7z48D5pK7+VYC9gQMl7VpneV8kdVd/JZe3SWns6leB2yLimdL+FwT+BKyYy90EWA64Mi8fBVyU9/dR4Fs5xrG1jsfMzMx6n85u/1P0d+D7pBZIgJDUBziYlIR1SUQ8Lukw4AhJV0TE/aVV9iElkXtFxCwASTsDT5JaPq9pp+irIuKk/PpoSVsCPyAlUpC65XeLiNdzmZcAn8zLdgWWBHZp6/rN+5wEfB04Na93RUScn5f/Mh/7OsDNwEHADRHRNgb0GUn7AHdI2r9Gl/JgQMCLEfFv4N+StgL+nZd3WF6hnJMi4rEck4CJwM7Asfn382XgkBr1tSUp6R4aEZPy9nsDu+VWz0OAMyOi7SKoSZIOAs4AjiwXlrfdG2DQUn25aolja+xy3lj5kIeatm8zM7PepJ4WyR8Cu0i6n9QCeSrwOPAZUtJTj+OAfwLnSepbWjYMuLstiQSIiKeAF4A1OyhzfOn9XaSWvDaT25LI7FU+aEkdBjxSTPYi4lXShUXFfT5dWB7AFKCt63odYJvcTfyapNeAm4C+QK3bKN0A/A34s6TJks4FBgCT6yzv/ZbGHNNFpNZJgE8B/YHLa+x/TeD5tiQyb/94RIyNiBl5/3uX9n8esIyk/uXCIuLsiFg/Itb/0GIL1NidmZmZzW+63CIZEQ9IWg34X+CpvO0lwGkR8WI9O42IdyXtAdxH7a7SqDFvAeDdDoqdVXrfh9Tt3mZGjW2K4/26ss9a+28rY0HgXNJFSWXPl2fkZG0zSRsD25C63K8lJdn7d6G85fPrmaVlFwI/lfRRUkvruFIC3WYmsx9/2YKkrv+Layx7o4PtzMzMrJeo6z6SEfFaRBwREV+OiC9GxE/rTSILZT1M6iI9mDRGsM0jwEbFlkpJq5NuNzShgyLXL70fCTzYxXAeAYYVW9okfYjU9dvRPosmAMMiYmLbBAwETgAWKq8saQtJR0fE3yPi0IgYBZwJbFelvDa59fYOUhL5eVJi2V68yxXHUypd5PSypMF5+eql/Q8DfkYa62pmZma9XGfP2j6jqwVFxL4V9v8L0sUi6xTmnUkaa3eupCNJYxfPICWFf+6grD0l3QXcRhrXOAL4RhfjuAj4MXBJYfzhMcBrwO+6WMYxwP2Sjs/HMJB00c8TEfFmjfVnAAdImkpq2f0waczm37pSnqQBHcRyAXAs8A5wYzvr/BW4F7hA0veB/wInA49FxCRJvwCulvQwqWt8FeBs4NLchW5mZma9XGctkmt0cVq9ys4jYiawB4Vu6dyithHwIdIFPleSxjtuXhw3WcMFuayHSbcq2j4inuxiHK/mfU4F/pKnKcBGXb3vYkQ8COwAjMox/AG4hZTU1lr/VmBMXv4YcDXpop0fVimv5FJSq+XF7dVZTga/QBorehtwO/ASKbEnIq4Dds8xPkYaH3keqdvdzMzMDM0PjUuSJgJnRcTRzY7FYPigReLab63atP37qm0zM7PGkXRfRJSHEAL13f4Hpedk70pqhZwJPABcFhEdXQRjZmZmZvOhep61vQ7wL9LYu42AzYCzgIfzxRlmZmZm1ovU0yJ5OulCkG9ExNsAkpYijU08nXQ/yaaIiCHN2reZmZlZb1VPIrkesEdbEgkQEa/np53c1/DIzMzMzKxHq+c+kpOY/X6PbT5MeiyfmZmZmfUi9bRI/gw4Oz8n+xbSLXvWJ93v8NeSNmxbMSLubmSQZmZmZtbz1JNIXpR/nsMHjxNse8TeEXkiL/PDls3MzMzmc/UkkkO7LQozMzMzazldTiQjYlJ3BmJmZmZmraWzZ20/QnpM4BuSHuWDLu05RMSajQ7OzMzMzHquzlokLwX+W3jd+s9TNDMzM7OG6DCRjIjDC2/PBZ6L0sO5JS0AfLwbYjMzMzOzHqye+0g+AyxdY/5ywK2NCcfMzMzMWkVnYyT3Ar7a9ha4WtLM0mqDgBe6ITYzMzMz68E6GyN5FbAuKYkcDTwFTC8sD+B+4OLuCM7MzMzMeq7OxkhOAfYFkPQC8MuImN7RNmZmZmbWO3TWtb1h4e31wHBJNdf1YxHNzMzMepfOurbvJHVfq/CzFj8W0czMzKyX6SyR9GMRzczMzKymzsZI+rGIZmZmZlZTl5+17UckmpmZmVlRlxNJ4JIa234M2BI4pGERmZmZmVlL6HIiWXpc4vsk/Q+wOXBqo4IyMzMzs56vnkcktuc6YLsGlGNmZmZmLaQRieS2wOsNKMfMzMzMWsjcXGwjYHFgBWBsg+MyMzMzsx5ubi62CWAacE9E3NK4kKzVLbT8Wqx8yL3NDsPMzMy6WWePSPxy4e2j7ay2rKSdIuLyxoVlZmZmZj1dZy2SlzBnd3YtATiRNDMzM+tFOrvY5kZgBvAX4JvAgIjoU2Pyc7bNzMzMepkOE8mI2AYYBFwG7Aw8J+k6SV+XtMS8CNDMzMzMeqZOb/8TEa9GxK8jYmUt6UIAABoESURBVGtgMDAO2A14QdI4SbtKWry7AzUzMzOznqWu+0hGxJSIODsitiAlleNJT7R5sRtiMzMzM7MerJ7b/wCQu7R3AHYEtgFeBS5qcFxmZmZm1sN1KZGUtDTwOeBLwFbAFOD3wNbAbRERHWxuZmZmZvOhzu4j+Q1Sy+OngFdIyeOWOHk0MzMz6/U6a5H8FfBf0u1/bgXeAzYGNpZmv6VkRBzTHQGamZmZWc/UWSL5LOlm42vmqT0BOJE0MzMz60U6TCQjYsg8isPMzMzMWkxdt/8xMzMzM2vjRNLMzMzMKnEiaWZmZmaV1H1DcrPOPDp5Cuvtf2GzwzAzsxZy37Fjmh2CVeAWSTMzMzOrxImkmZmZmVXiRNLMzMzMKnEiaWZmZmaVOJE0MzMzs0qcSJqZmZlZJU4kzczMzKwSJ5JmZmZmVokTSTMzMzOrxImkmZmZmVXiRNLMzMzMKnEiaWZmZmaVOJE0MzMzs0qcSJqZmZlZJU4kzczMzKwSJ5JmZmZmVokTSTMzMzOrxImkmZmZmVXiRNLMzMzMKnEiaWZmZmaVOJE0MzMzs0qcSJqZmZlZJU4kzczMzKwSJ5JmZmZmVokTSTMzMzOrxImkmZmZmVXiRNLMzMzMKnEiOZ+RdLKk/0pau8aylSW9KenkZsRmZmZm8xcnkvOfscBk4GxJ5d/vmcDzwI/neVRmZmY233EiOZ+JiLeBbwIbAfu0zZf0FWAbYPeImNak8MzMzGw+4kRyPhQRNwHnAEdJGiSpP3AScHxE3AEgaRFJp0h6UdI7km6VtHFbGZI+JulPkt6QNFXSVZIGNeeIzMzMrCdyIjn/+hHwJvBL4ChgKnBIYflpwObATsAawDjgRkkfzcsvAV4D1gU2BhYHzp8XgZuZmVlrWLDZAVj3iIjXJe1DShBnAptGxHRIF90AuwPrRcQ/8ybHSRpN6hbfHxgC3AU8HxFvS/oGsHx7+5O0N7A3wKCl+nLVEsd2x2GZdWrlQx5qdghmZr2GE8n5WERcI+keYFJE3FNYtDapNXq8pOImiwDv5teHACcAu0m6BbgeuKCDfZ0NnA0wfNAi0bCDMDMzsx7LieT87x2gfHFN2+99BDCjxvpExKmSrgR2ADYDfgbsJ2mtfEGPmZmZ9XIeI9k7PZJ/DoqIiW0T8H1gG0n9JZ0ELBkRZ0XELsCWwGBgzeaEbGZmZj2NE8leKCKeBK4AzpG0Vb5R+cGk2wU9QrrIZjvgNElrSxoM7Aq8AjzarLjNzMysZ3HXdu+1J3AMcBHpiuwJwGfbxlJK2p40RvIW0nlyD7BVRLzVnHDNzMx6lpkzZzJ58mSmT5/e7FAaol+/fqy44or07du3y9s4kZzPRcTodua/SWqB3Ked5U+SxkeamZlZDZMnT2aJJZZgyJAhlC5ebTkRwZQpU5g8eTJDhw7t8nbu2jYzMzOrYPr06QwYMKDlk0gASQwYMKDu1lUnkmZmZmYVzQ9JZJsqx+KubTMzM7MGWG//Cxta3n3Hjul0nfHjx/OFL3yBhx56iBVXXBGAn/zkJ6y66qrsvvvuDY2nFrdImpmZmbWwvn37sueeexIx758H4kTSzMzMrIV96lOfon///pxxxhmzzT/ooIPYeOONGTFiBKeddlq37Ntd22ZmZmYt7owzzmDEiBFstdVWALz11ls88cQT3HHHHcyaNYtRo0ax2Wabsfbaazd0v26RNDMzM2txAwYM4JRTTmHMmDG8++67TJs2jVGjRiGJvn37MnLkSB577LGG79eJpJmZmdl8YLvttmPYsGGce+659OvXj1tuuYWIYObMmdx+++2sscYaDd+nu7bNzMzM5hMnnHACN910E0suuSSrrroqI0eOJCLYZZddGDZsWMP350TSzMzMrAG6crueRhs9ejSjR49+//0SSyzB008/Pc/2765tMzMzM6vEiaSZmZmZVeJE0szMzMwqcSJpZmZmZpU4kTQzMzOzSpxImpmZmVklvv2PmZmZWQM8+7PGPn5w5UMe6nSdHXfckQ022IADDzwQgDfeeIP11luPK6+8suGPQ6zFLZJmZmZmLeqss87izDPP5PHHHwfghz/8Id/+9rfnSRIJTiTNzMzMWtbAgQM59dRT2WuvvbjpppuYNGkS3/ve9xg/fjwjR45kiy22YMyYMcyYMYMnn3ySTTbZhJEjR7Lpppvy3HPPzfX+3bVtZmZm1sJ22GEHrrrqKvbaay9uv/12AL71rW8xfvx4ll9+eU488UTOOuss+vTpw3rrrcfxxx/P7bffztSpU1lppZXmat9OJM3MzMxa3JgxY5g2bRorrLACL7/8Mv/5z3/YZZddAJg+fTqjRo3i8MMP58QTT2S77bZjscUW46ijjprr/TqRNDMzM5uPDBgwgJVWWolx48ax1FJLcf3119O3b1/GjRvH8OHDGTt2LBdffDEnnHAC55xzzlzty4mkmZmZ2XykT58+nHLKKWy//fa89957LLbYYlx44YVMmzaNPfbYg+OPP54ZM2Zw0kknzfW+FBENCNnsA8MHLRLXfmvVZodhvVRXbpdhZtYIjz76KGussUazw2ioWsck6b6IWL/W+r5q28zMzMwqcSJpZmZmZpV4jKQ13ELLr8XKh9zb7DDMzMysm7lF0szMzKyi+elakyrH4kTSzMzMrIJ+/foxZcqU+SKZjAimTJlCv3796trOXdtmZmZmFay44opMnjyZl19+udmhNES/fv1YccUV69rGiaSZmZlZBX379mXo0KHNDqOp3LVtZmZmZpU4kTQzMzOzSpxImpmZmVklfkSiNZykN4HHmx1HDzAQeKXZQfQQrovE9ZC4HhLXwwdcF0lPrYfBEbFMrQW+2Ma6w+PtPZOzN5F0r+shcV0krofE9ZC4Hj7gukhasR7ctW1mZmZmlTiRNDMzM7NKnEhadzi72QH0EK6HD7guEtdD4npIXA8fcF0kLVcPvtjGzMzMzCpxi6SZmZmZVeJE0uoiaUFJx0h6SdLbkv4gaYW8bBFJl0l6U9LtkoYUtusn6RlJqzYr9kaStIqkqDFtquQMSa9LekDSJwrbSdI9kjZrZvyNIOlXks4qzdtW0sOSZkiaIOmzpeV7SnpB0r8l7VpadrykQ+dF7I3UTj3cWuPcOKewfFtJEyW9Imn/0rbfkXTevIp/bklaXNIJkp6V9EY+9o0Ky3vFOdGFeugV54SkQfl7YGr+vZ4haYnC8t0kPSVpev5bOLK0/U8kTcnrbFla9ntJu82rY5lbXaiL52qcEz8pLG+Nz0ZEePLU5Qk4EngWGAmsAYwH7szLvgPcC3wEOBO4vLDdAcCZzY6/gfXwOWAq6Z5fxWlBYAdgUq6fg4F7Ctt9Gbi+2fHP5bELOBQI4KzC/LWAGcB+wMr5d/5fYJ28fCDwDrAVsAXwNjAgL1spn1eLN/v45rYe8rJXga+Wzo3F87I+wEvAHsC6wOvAsLxs8VwPKzf7+Oqoh0uBR4FR+bN/AvAWMLQ3nRMd1UNvOSfyZ+KfwI35d782cDNwRV6+Vf79fxUYDJyUj3W5vHxt4E1gPWA34EU+GIK3IfAQ0KfZx9mgulgq/+34ZOmcWCQvb5nPRtMD8NQ6E7AQ8AYwpjDvI/nDsAkpefxFnr81MCG/Xhp4Dli+2cfQwLr4CXBHO8sOBH6XX38MeDu/XhB4DPh4s+Ofi+Memv8Yvpj/kBUTybOBv5bWvwX4VX49Ani+sOx5YIP8+jzgu80+vgbVw4r5M/HRdrZdNi9fOL//O7BTfn0ocEKzj6+OehgAvAdsVZr/JDC2t5wTXaiHXnFOAMsBlwEfKcz7PDALWISUVJ1bWCbgGeDg/H5n4O/59cK5TpbJ728GPtvsY2xgXWwKvAv0a2f7lvlsuGvb6vFxYAnSBxqAiHia1Pq2KekPwrqSFgA2yu8BDgJ+ExHPz9twu9XapKSwlmeAYZL6MXs97AXcHxH/nAfxdZdNSInTJ4CnS8s2pXBuZDfn+eTt+ksaKmkVoD/wrKS1SP+Vn0Xr6Kge1gZm1pjf5hVSS9UGkgYAqwHPSFoG+CZwVLdE3D2mA58B7ijNF6klrbecE53VQ684JyLihYj4cv5eQNKHgX1IrWmzSJ+b4vdHkP6xaDsfngFWkdSf9LfzDWCKpG1ISfbV8+xg5lIndTGTdE5Miojp7RTROp+NZmeynlpnAnYk/dfdpzT/VuA0Usvj3aSui2dJH5QVgMnA0s2Ov8F18QhwNalrfzJwPbBuXrYg8MdcD1OATwGLkhLuVZod+1wetwqvxzN7S9xbwB6l9b8BvFV4/9NcLzOAH+V54yi0crfC1Ek9HAD8B/gVMBH4B/Dt0jZ7krqt/guclOedDBzS7GNrQN18ntSSNKo3nROd1EOvOyeAK/P3xRukIT0Dc31sXlrv58DDhfe/ynXwFrALKRn/BzCq2cfUqLrI884gddVfSuqxuwvYubRdS3w2mh6Ap9aZgK8DM2rMvxH4deH9AD4Y13I2sD+pC/w20n+c+zX7WOayHhYi/Ud5KylJXBc4l/Sf5kcL6/UHFsivxwKn5z+m15ES7WOKXyStNjFnAvUusEtpna8C75bmLQYsml+PBB4E+uYvzeeAq4D+zT6+uaiHC/MXxh7AMFJL9Bvk7rvCegsDS+TXQ/I5sXg+VyYCf6UFxsWVjmlUPta2cWC99Zwo10OvOydI4wLXAy4hNTQMJiWSG5fWGwv8qzRvKWChwvlyHak7+KJ8PvyaPAygFaYaddEH+Bvpn4sd8znxQ9L3yq6lbXv8Z6PpFeypdaZ8wgdztkj+DTi1xvofy3/8+uU/BAeSWi2fJbfeteqU/6j1LbwX8DBwYo11PwT8mzRm5nTSf6KLkC5M+kKzj2Uu6mA8c7ZI7llaZ0/gzQ7KuJV0cdJOpFaHRYBTgJObfXxzUQ99KA2CJ7VIvdpBGb8hXay2QT5XPpS/WMY1+/jqqIctSf9M3QYs1lvPiXbqoVeeE/k4FiF13e+cvz8+VVr+M+ChdrbtSxpnug6pQeJa0j/yf6AFGyRKdbEgOUEsLD8D+EcH2/fIz4bHSFo9nss/B5XmDyosKzoSODLSGJBNgWsi4jXSOKJNa6zfMiLinYiYWXgfpO7uFWusPhY4PyJeIB33tRHxDvAX0liX+cVzdP3cQNJnSC2y15Dq5c+5Xv5IC9dLRLwXEW+VZj8MLC1psfL6koYDGwP/R6qH2yNiKumfr5aoh3xLlutISfXWEfF2XtSrzon26qG3nBOSlpX0leK8/Pt7hjTmcRp1nA/At4C7I+IBUj38MSL+C9xAD64H6LwuImJWREwrbfYwtb9DevRnw4mk1eOfpP+0N2+bIWkwH3RbU5i/Iak5/7w8K0itdpD+o1you4PtLpLWUbqH5ojCvAVIXdwTSuuuRBrnc0yeNd/UQw23UTg3ss0pnRsAkvqQLiD4cZ4139SLpDsknV6avSFpYP3bNTb5BXBY/oJsuXqQNIb0Ob+QdFVt8Rh7zTnRUT30onNiZeB3ktZtmyFpaVLv1AukRoTi94eA0dQ+HxYn9WL9NM9qpXqATuoi30PygNI2G1L6Dsnb9ezPRrObej211kRKiJ4lXX33UVKr2hy3wSGN5dmx8P4G4FhSF8UUYGSzj2Uu6qAPcF+eNiD9YTgfeBkYWFr3XPIg6fz+LODiXHdPAF9t9vHMRT2MZ/Yu3XVIA8P3I93nbD/SIPG1a2w7htQy2/b+K6Sr4D9KGnx+drOPby7q4fv5uL9GGhe2G6mLd7ca244ijXnqk99vlM+jtUn3Ibyx2cfXybEvR2pl+iNz3lN18d5yTnShHnrFOUFKbsYD95DuaDAc+DMpifwwsF0+H3bN58NxpPtrLlujrEOA0wrvD8plr0Lq4h3b7OOdy7o4iXQv4s+QxsPuRxojObpGWT36s9H0yvbUWhNpzMqJpHEeb5OuRluutM7WFG7CneetThrT8Qbw82YfRwPqYTngAtJNhKeRLjgaVlpnTfIY0cK85fMfl7dJA8Zb4ua67dTBeOa8EfdnSV38M0jdNNvV2G5h0m1QhhfmLQCcQ7oZ8a21vlh66tROPXwHeDzXwxPA3u1sewfwmdK8I4DXgAeAjzX7+Do59n1JrSO1pvN7yznRxXroLefEAFLL7Et5uoTCBUKkC42ezvVwNzCiRhkDSReiLFuYtzhpbOTbpCuXF+3uY+nOuiCNkTyM1NU9I/9u5xgz3wqfjbYra83MzMzM6uIxkmZmZmZWiRNJMzMzM6vEiaSZmZmZVeJE0szMzMwqcSJpZmZmZpU4kTQzMzOzSpxImpm1CEmDJX212XF0RtJ2kiZJmiHpe/N435K0h6QPz8v9mvVWvo+kmVmLkHQ98GJE7N7sWDoi6VbSjaP3AV6JiDfn4b5HAHcCQyNi4rzar1lvtWCzAzAzsy5T56v0CAOBv0bEM03Yd6vUkdl8wV3bZtbrSApJ35J0f+5+fUDSp0vr7CTp4bz8aUkHSFJeNlrSLEljJb0laXyev4KkiyW9JmmqpEslrVBHmdMlfU7SY5LelnS7pI3y8vNJjx/dTVIUyvxRXv+dvM/fF7t1JW0i6d5c9iOSds/HPyQv7yPpMEnP5XXuk/SZTupvI0njJb0h6QVJJ0tatK1uSY9EPaQYZ2l75bj/leO+X9L2hXoIScsV1p9tXu46/0fe9j+STpTUNx/T3/Nmz0g6LK8/SNLvcqxvSrpK0tBC+RMl7S/pz7nMZ/LvYacc41uSrpM0oLDNOpJukjRN0suS/k/SUoXlIennkl7MMS7bXtwd1bVZj9fsZ1F68uTJ07yeSM9AfhX4GrAqcDLpmemD8vJRpOff/g8wGNgSeAo4OC8fncu4HlgFWIvUw/MQ8DdgPWAN4BbgzjrKfI/ULbsh8FHSc3Qn5uWLAzcBvwMG5nnfAZ4HPk16jvunSc/0PTUvX4b0fPszgdWAz+flAQzJ6/wUeBbYDlgZ2B14CxjVTt1tDMwEjs51tyXpWcDX5uUDgSeBY9rirFHGgaRnR++c6+KgXObahbpdrrD++/OADwPTcxkrAhsBk4GDSY0jW+d1PwEsCiydl18PDAfWAW7M85bO5U/M9fR1YAhweX5/Zy5n/RzvkXn9/sArwOm5Xj8O3AD8uXSOTcrbr9dR3M3+PHjyNDdT0wPw5MmTp3k95S/5Iwvv++Tk56j8/i/ASaVtdgReyq/bEptRheXbkhLBwYV5HwOOAhauo8xPlpZHIeG5ATi/sHx7YMtSmecBN+bXhwL/AhYoLN83lzkkx/Um8PlSGacBl7dTd5cDt5bmbZHLXDu/fww4rIP6fwH4SWneYcAGdJ5IrpNf7wH0ycs/DqyWX2/E7Inyt0lJ4dKF8vrned/J7ycCvyks367G7/dyYFx+/RPgn6X4B+Zt1iqcY4cUlncYtydPrTq5a9vMeqvxbS8i4j3gHlIrIqQv/b1zF/Vrkl4jJWjLSOpfKKM4BnBN4PmImFQo9/GIGBsRM+oo8+nC61fyz4VrHUBEXAe8Jumnki6T9CAwBlgorzKc1CL6bmGz2wuvVyW1dF5YiusbpBbRWoaVygC4jZQkrdnONu/L3cPLAneVjuWwiLins+0j4gHgEuBc4CVJvyN1pbc3HnMY8EhEvFYo41VgQinexwuvp+WfTxXmvc0Hv4d1gDVLdda2brHe3o+pQtxmLcEX25hZbzWr9L4PqXsV0t/GI4CLa2z3RuH1zNLrji706GqZ79ZYXrPcPAbwB8BvSN21RwPfI3UXt21XbjAoltX2HfB5Zk9gYfZjKyuPfeyTy60Ve1lbue3VVdRYPtt3VUTsIuloUsvhZsBvgS8BO3UxXoAFSvGWzwdILcy1LEhqHf5ujWUvF17PVocV4jbr8dwiaWa91fptLyQtQBqX+GCeNQFYPSImtk2klq2f0X5yMQFYTtLKhXJXzxdiDK5YZlk5IfoeMDYivh0R5wD/ILVCtiVhDwLrSyr+rd+w8PpJUrIzpBTXV4Fd24nhEeCTpXmj8s8JnR5AxBuksYHFOJB0s6Qf8UFr4OKFxR8trLeupNOBCRHxi4jYBvgxKTmDOevoEWBYsdVX0odI41o7jbcdE/L2kwt1Ngs4lTQudQ5diNusJTmRNLPe6iBJO0halXTRxJL5J8AvgF2VrqoeKmkL4GxgSkS0d/PdvwL3AhfkK3rXAE4BHsvd3VXKLJsGDJU0KL9/HthW0qqS1gL+j5Tg9MvLTydd5HFyXuczwM/zsoiIaaQLjX4paUdJK0v6Jmm84hPtxHAisJGkY3OZm+f9/DEiHu3icfwS+KGkL+V9fp80tvF64FFSN/KBSjdg35aUMLd5ndT1flyux2Gk8al/K9QRwHBJSwIXkcaBXiJpuKThpFbh10gXLlVxGilhvEDSmrnM3wIrkC6wqaWzuM1aU7MHaXry5MnTvJ5IrVZHk8ZFvkMa4/eJ0jpfBx4mXWn9HHAksGBeNprSBSF5/iDgSlLiMpWUxCxTtczyPFLr1QukZGk5UqvqXfkYJgGHA3uTErHF8jYb5+OcQWpJ+1kuc5m8vC/pgqDJpKuKHwa+2kn9bUIaY/oGaYzfEcCiheWdXWwjYCzpavF3gLuBLQrLtye1JM7Ix7dDqR62ydu8RboK/cLC8SwAXJuP5aQ8b2VS0vhSnn4LrFTY30TgoPbqPc87H7ih8H4EcHP+XUzJZS5bOse+UjruduP25KlVJz/Zxsx6HaX7G+4SEZc0O5bulFtF+0fEHYV5XyO1XC4Zs1+EY2ZWN3dtm5nNv1YCbpb0BaWbpW9Cut/i751Emlkj+KptM7P5VETcKGl/Ujf+YFIX7OWkbmUzs7nmrm0zMzMzq8Rd22ZmZmZWiRNJMzMzM6vEiaSZmZmZVeJE0szMzMwqcSJpZmZmZpU4kTQzMzOzSv4fkR8vtPgJsxoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 648x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9, 4.5))\n",
    "barplot_percentages(\"MultipleLines\", orient=\"h\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = [\"PhoneService\",\"MultipleLines\",\"OnlineSecurity\", \"OnlineBackup\", \"DeviceProtection\", \"TechSupport\", \"StreamingTV\", \"StreamingMovies\"]\n",
    "df1 = pd.melt(data[data[\"InternetService\"] != \"No\"][cols])\n",
    "df1.rename(columns={'value': 'Has service'},inplace=True)\n",
    "plt.figure(figsize=(20, 8))\n",
    "ax = sns.countplot(data=df1, x='variable', hue='Has service')\n",
    "ax.set(xlabel='Internet Additional service', ylabel='Num of customers')\n",
    "plt.rcParams.update({'font.size':15})\n",
    "plt.legend( labels = ['No Service', 'Has Service'],fontsize=10)\n",
    "plt.title('Num of Customers as Internet Additional Service')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 8))\n",
    "df1 = data[(data.InternetService != \"No\") & (data.Churn == \"Yes\")]\n",
    "df1 = pd.melt(df1[cols])\n",
    "df1.rename(columns={'value': 'Has service'}, inplace=True)\n",
    "ax = sns.countplot(data=df1, x='variable', hue='Has service', hue_order=['No', 'Yes'])\n",
    "ax.set(xlabel='Internet Additional service', ylabel='Churn Num')\n",
    "plt.rcParams.update({'font.size':15})\n",
    "plt.legend( labels = ['No Service', 'Has Service'],fontsize=10)\n",
    "plt.title('Num of Churn Customers as Internet Additional Service')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               PaymentMethod Churn  percentage of customers\n",
      "0  Bank transfer (automatic)    No                 0.182593\n",
      "1  Bank transfer (automatic)   Yes                 0.036632\n",
      "2    Credit card (automatic)    No                 0.183161\n",
      "3    Credit card (automatic)   Yes                 0.032941\n",
      "4           Electronic check    No                 0.183729\n",
      "5           Electronic check   Yes                 0.152066\n",
      "6               Mailed check    No                 0.185148\n",
      "7               Mailed check   Yes                 0.043731\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9, 4.5))\n",
    "barplot_percentages(\"PaymentMethod\",orient='h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 777.6x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(data, col=\"PaperlessBilling\", height=6, aspect=.9)\n",
    "ax = g.map(sns.barplot, \"Contract\", \"churn_rate\", palette = \"Blues_d\", order= ['Month-to-month', 'One year', 'Two year'])\n",
    "plt.rcParams.update({'font.size':18})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kdeplot('MonthlyCharges','MonthlyCharges')\n",
    "kdeplot('TotalCharges','TotalCharges')\n",
    "#设置字体大小\n",
    "# 解决中文显示问题\n",
    "plt.rcParams['font.family'] = ['sans-serif']\n",
    "plt.rcParams['font.sans-serif'] = ['Arial Unicode MS']\n",
    "#设置字体大小\n",
    "plt.rcParams.update({'font.size': 15})\n",
    "plt.legend(fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "中低消费    1766\n",
       "低消费     1762\n",
       "高消费     1758\n",
       "中高消费    1757\n",
       "Name: level_MonthlyCharges, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "      <th>churn_rate</th>\n",
       "      <th>level_MonthlyCharges</th>\n",
       "      <th>level_TotalCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2499</th>\n",
       "      <td>6061-GWWAV</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>70.20</td>\n",
       "      <td>2894.55</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>中低消费</td>\n",
       "      <td>中高消费</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3967</th>\n",
       "      <td>5914-GXMDA</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>32</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>...</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>19.30</td>\n",
       "      <td>593.20</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>低消费</td>\n",
       "      <td>中低消费</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5231</th>\n",
       "      <td>1075-BGWOH</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>16</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>98.75</td>\n",
       "      <td>1587.55</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>高消费</td>\n",
       "      <td>中高消费</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5844</th>\n",
       "      <td>2905-KFQUV</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>70.40</td>\n",
       "      <td>154.80</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>中高消费</td>\n",
       "      <td>低消费</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4674</th>\n",
       "      <td>0980-FEXWF</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>26</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>50.35</td>\n",
       "      <td>1285.80</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>中低消费</td>\n",
       "      <td>中低消费</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
       "2499  6061-GWWAV    Male              0      No        Yes      41   \n",
       "3967  5914-GXMDA  Female              0     Yes         No      32   \n",
       "5231  1075-BGWOH    Male              1     Yes         No      16   \n",
       "5844  2905-KFQUV  Female              0     Yes         No       2   \n",
       "4674  0980-FEXWF    Male              0     Yes        Yes      26   \n",
       "\n",
       "     PhoneService MultipleLines InternetService       OnlineSecurity  ...  \\\n",
       "2499          Yes            No             DSL                  Yes  ...   \n",
       "3967          Yes            No              No  No internet service  ...   \n",
       "5231          Yes           Yes     Fiber optic                   No  ...   \n",
       "5844          Yes            No     Fiber optic                   No  ...   \n",
       "4674          Yes            No             DSL                   No  ...   \n",
       "\n",
       "          StreamingMovies        Contract PaperlessBilling  \\\n",
       "2499                   No        One year               No   \n",
       "3967  No internet service        One year               No   \n",
       "5231                  Yes  Month-to-month              Yes   \n",
       "5844                   No  Month-to-month              Yes   \n",
       "4674                   No        One year               No   \n",
       "\n",
       "                PaymentMethod MonthlyCharges TotalCharges Churn churn_rate  \\\n",
       "2499             Mailed check          70.20      2894.55    No          0   \n",
       "3967             Mailed check          19.30       593.20    No          0   \n",
       "5231         Electronic check          98.75      1587.55   Yes          1   \n",
       "5844  Credit card (automatic)          70.40       154.80    No          0   \n",
       "4674             Mailed check          50.35      1285.80    No          0   \n",
       "\n",
       "      level_MonthlyCharges  level_TotalCharges  \n",
       "2499                  中低消费                中高消费  \n",
       "3967                   低消费                中低消费  \n",
       "5231                   高消费                中高消费  \n",
       "5844                  中高消费                 低消费  \n",
       "4674                  中低消费                中低消费  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def charge_to_level(charge):\n",
    "    if charge<=da.loc[\"25%\"]:\n",
    "        return \"低消费\"\n",
    "    elif charge<=da.loc[\"50%\"] and charge>da.loc[\"25%\"]:\n",
    "        return \"中低消费\"\n",
    "    elif charge<=da.loc[\"75%\"] and charge>da.loc[\"50%\"]:\n",
    "        return \"中高消费\"\n",
    "    else:\n",
    "        return \"高消费\"\n",
    "da=data[\"MonthlyCharges\"].describe()\n",
    "data[\"level_MonthlyCharges\"] = data[\"MonthlyCharges\"].apply(charge_to_level)\n",
    "da=data[\"TotalCharges\"].describe()\n",
    "data[\"level_TotalCharges\"] = data[\"TotalCharges\"].apply(charge_to_level)\n",
    "\n",
    "\n",
    "display(data[\"level_MonthlyCharges\"].value_counts())\n",
    "data.sample(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(20,10),dpi=80)\n",
    "# 子图1\n",
    "fig.add_subplot(2,2,1)\n",
    "sns.countplot(x=\"level_MonthlyCharges\",hue=\"Churn\",data=data,order=[\"低消费\",\"中低消费\",\"中高消费\",\"高消费\"])\n",
    "\n",
    "# 子图2\n",
    "fig.add_subplot(2,2,2)\n",
    "sns.countplot(x=\"level_TotalCharges\",hue=\"Churn\",data=data,order=[\"低消费\",\"中低消费\",\"中高消费\",\"高消费\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 丢掉customerID特征\n",
    "customerID=data['customerID']\n",
    "data.drop(['customerID'],axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>Churn</th>\n",
       "      <th>level_MonthlyCharges</th>\n",
       "      <th>level_TotalCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>No</td>\n",
       "      <td>低消费</td>\n",
       "      <td>低消费</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>No</td>\n",
       "      <td>中低消费</td>\n",
       "      <td>中高消费</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>Yes</td>\n",
       "      <td>中低消费</td>\n",
       "      <td>低消费</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  SeniorCitizen Partner Dependents PhoneService     MultipleLines  \\\n",
       "0  Female              0     Yes         No           No  No phone service   \n",
       "1    Male              0      No         No          Yes                No   \n",
       "2    Male              0      No         No          Yes                No   \n",
       "\n",
       "  InternetService OnlineSecurity OnlineBackup DeviceProtection TechSupport  \\\n",
       "0             DSL             No          Yes               No          No   \n",
       "1             DSL            Yes           No              Yes          No   \n",
       "2             DSL            Yes          Yes               No          No   \n",
       "\n",
       "  StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0          No              No  Month-to-month              Yes   \n",
       "1          No              No        One year               No   \n",
       "2          No              No  Month-to-month              Yes   \n",
       "\n",
       "      PaymentMethod Churn level_MonthlyCharges level_TotalCharges  \n",
       "0  Electronic check    No                  低消费                低消费  \n",
       "1      Mailed check    No                 中低消费               中高消费  \n",
       "2      Mailed check   Yes                 中低消费                低消费  "
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对离散特征进行one—hot编码\n",
    "cateCols = [c for c in data.columns if data[c].dtype == 'object' or c == 'SeniorCitizen']\n",
    "dfCate = data[cateCols].copy()\n",
    "dfCate.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 绘制关联图 \n",
    "# plt.figure(figsize=(16,8))\n",
    "# dfCate.corr()['Churn'].sort_values(ascending=False).plot(kind='bar')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 特征选择\n",
    "dropFea = ['gender','PhoneService',\n",
    "#            'OnlineSecurity_No internet service', 'OnlineBackup_No internet service',\n",
    "#            'DeviceProtection_No internet service', 'TechSupport_No internet service',\n",
    "#            'StreamingTV_No internet service', 'StreamingMovies_No internet service',\n",
    "           'OnlineSecurity', 'OnlineBackup',\n",
    "           'DeviceProtection','TechSupport',\n",
    "           'StreamingTV', 'StreamingMovies',\n",
    "           ]\n",
    "dfCate.drop(dropFea, inplace=True, axis =1) \n",
    "# 最后一列是作为标识\n",
    "target = dfCate['Churn'].values\n",
    "#列表：特征和1个标识\n",
    "columns = dfCate.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构造训练数据集和测试数据集。\n",
    "# 列表：特征\n",
    "columns.remove('Churn')\n",
    "# 含有特征的DataFrame\n",
    "features = dfCate[columns].values\n",
    "# 30% 作为测试集，其余作为训练集\n",
    "# random_state = 1表示重复试验随机得到的数据集始终不变\n",
    "# stratify = target 表示按标识的类别，作为训练数据集、测试数据集内部的分配比例\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(features, target, test_size=0.30, stratify = target, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier #KNN分类模型\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score # 评估指标 --正确率 精准率 召回率 F1调和平均值\n",
    "\n",
    "# 构造各种分类器\n",
    "classifiers = [\n",
    "    SVC(random_state = 1, kernel = 'rbf'),    \n",
    "    DecisionTreeClassifier(random_state = 1, criterion = 'gini'),\n",
    "    RandomForestClassifier(random_state = 1, criterion = 'gini'),\n",
    "    KNeighborsClassifier(metric = 'minkowski'),\n",
    "    AdaBoostClassifier(random_state = 1),   \n",
    "]\n",
    "# 分类器名称\n",
    "classifier_names = [\n",
    "            'svc', \n",
    "            'decisiontreeclassifier',\n",
    "            'randomforestclassifier',\n",
    "            'kneighborsclassifier',\n",
    "            'adaboostclassifier',\n",
    "]\n",
    "# 分类器参数\n",
    "#注意分类器的参数，字典键的格式，GridSearchCV对调优的参数格式是\"分类器名\"+\"__\"+\"参数名\"\n",
    "classifier_param_grid = [\n",
    "            {'svc__C':[0.1], 'svc__gamma':[0.01]},\n",
    "            {'decisiontreeclassifier__max_depth':[6,9,11]},\n",
    "            {'randomforestclassifier__n_estimators':range(1,11)} ,\n",
    "            {'kneighborsclassifier__n_neighbors':[4,6,8]},\n",
    "            {'adaboostclassifier__n_estimators':[70,80,90]}\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'No'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-356-8118d7572120>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     29\u001b[0m             \u001b[0;34m(\u001b[0m\u001b[0mmodel_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m     ])\n\u001b[0;32m---> 31\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGridSearchCV_work\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpipeline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_y\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_y\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_param_grid\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'accuracy'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-356-8118d7572120>\u001b[0m in \u001b[0;36mGridSearchCV_work\u001b[0;34m(pipeline, train_x, train_y, test_x, test_y, param_grid, score)\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0mgridsearch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpipeline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_grid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparam_grid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscoring\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscore\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;31m# 寻找最优的参数 和最优的准确率分数\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0msearch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgridsearch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"GridSearch 最优参数：\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msearch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"GridSearch 最优分数： %0.4lf\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0msearch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_score_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     70\u001b[0m                           FutureWarning)\n\u001b[1;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m    763\u001b[0m             \u001b[0mrefit_start_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    764\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 765\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    766\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    767\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    333\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_final_estimator\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'passthrough'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    334\u001b[0m                 \u001b[0mfit_params_last_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfit_params_steps\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 335\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_final_estimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params_last_step\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    336\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    337\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/sklearn/svm/_base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    160\u001b[0m             X, y = self._validate_data(X, y, dtype=np.float64,\n\u001b[1;32m    161\u001b[0m                                        \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'C'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 162\u001b[0;31m                                        accept_large_sparse=False)\n\u001b[0m\u001b[1;32m    163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    430\u001b[0m                 \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    431\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 432\u001b[0;31m                 \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    433\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    434\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     70\u001b[0m                           FutureWarning)\n\u001b[1;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[1;32m    800\u001b[0m                     \u001b[0mensure_min_samples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mensure_min_samples\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    801\u001b[0m                     \u001b[0mensure_min_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mensure_min_features\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 802\u001b[0;31m                     estimator=estimator)\n\u001b[0m\u001b[1;32m    803\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    804\u001b[0m         y = check_array(y, accept_sparse='csr', force_all_finite=True,\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     70\u001b[0m                           FutureWarning)\n\u001b[1;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[1;32m    596\u001b[0m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"unsafe\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 598\u001b[0;31m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    599\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m                 raise ValueError(\"Complex data not supported\\n\"\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/numpy/core/_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m     \"\"\"\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'No'"
     ]
    }
   ],
   "source": [
    "# 对具体的分类器进行 GridSearchCV 参数调优\n",
    "from sklearn.model_selection import GridSearchCV #网格交叉验证\n",
    "from sklearn.pipeline import Pipeline #引入流水线\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler # StandardScaler：均值标准差标准化 # MinMaxScaler：最小最大值标准化\n",
    "from sklearn.decomposition import PCA\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# 对具体的分类器进行 GridSearchCV 参数调优\n",
    "def GridSearchCV_work(pipeline, train_x, train_y, test_x, test_y, param_grid, score = 'accuracy_score'):\n",
    "    response = {}\n",
    "    gridsearch = GridSearchCV(estimator = pipeline, param_grid = param_grid, cv=3, scoring = score)\n",
    "    # 寻找最优的参数 和最优的准确率分数\n",
    "    search = gridsearch.fit(train_x, train_y)\n",
    "    print(\"GridSearch 最优参数：\", search.best_params_)\n",
    "    print(\"GridSearch 最优分数： %0.4lf\" %search.best_score_)\n",
    "    #采用predict函数（特征是测试数据集）来预测标识，预测使用的参数是上一步得到的最优参数\n",
    "    predict_y = gridsearch.predict(test_x)\n",
    "    print(\" 准确率 %0.4lf\" %accuracy_score(test_y, predict_y))\n",
    "    response['predict_y'] = predict_y\n",
    "    response['accuracy_score'] = accuracy_score(test_y,predict_y)\n",
    "    return response\n",
    " \n",
    "for model, model_name, model_param_grid in zip(classifiers, classifier_names, classifier_param_grid):\n",
    "    #采用 StandardScaler 方法对数据规范化：均值为0，方差为1的正态分布\n",
    "    pipeline = Pipeline([\n",
    "            #('scaler', StandardScaler()),\n",
    "            #('pca',PCA),\n",
    "            (model_name, model)\n",
    "    ])\n",
    "    result = GridSearchCV_work(pipeline, train_x, train_y, test_x, test_y, model_param_grid , score = 'accuracy')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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